g. vasile , e. trouvé, i. petillot, ph. bolon, j.-m. nicolas,
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7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 1
High Resolution SAR Interferometry: estimation of local
frequencies in the context of Alpine glaciersG. VasileG. Vasile, E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,, E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,
M. Gay, J. Chanussot, T. Landes and P. Grussenmeyer M. Gay, J. Chanussot, T. Landes and P. Grussenmeyer
gabriel.vasile@univ-savoie.frgabriel.vasile@univ-savoie.fr
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 2
Outlines
Context: InSAR high resolution Local frequencies estimation algorithm Results and discussions
Low Resolution ERS TANDEM data High Resolution simulated TS-X data
Conclusions and perspectives
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 3
Low Resolution (LR) vs. High Resolution (HR)
Longitudinal elevation profiles along the Mer-de-glace (m)
LR – 80m LR+HR – 2mMer-de-glace surface
May 2004
• Strong topography -> narrow fringes
• Glacier microreliefmicrorelief -> HR component
Different surface penetration
Different orientations
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 4
Need for frequencies estimates - Estimation
Estimation of 2nd order moments : complex correlation
3 directions for preserving the stationarity & ergodicity Spatial support: boxcar, directional, region growing… Appropriate estimator: ML, LLMMSE… Compensation of deterministic phase components
STATIONARITY
ERGODICITY
Trade-off: ergodicity/stationarity – number of samplesTrade-off: ergodicity/stationarity – number of samples ! !
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 5
Need for frequencies estimates – 2D unwrapping
Phase ambiguity Wrapped phase: φ = Φ (mod 2π)
Nyquist criterion: | Φ(N) − Φ(M)| < π
Phase difference test for unwrapping:
Phase difference -> phase gradient -> Phase difference -> phase gradient -> local frequencylocal frequency
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 6
Outlines
Context: InSAR high resolution Local frequency estimation algorithm Results and discussions
Low Resolution ERS TANDEM data High Resolution simulated TS-X data
Conclusions and perspectives
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 7
Phase LR+HR model
Analytical phase signal:
: 2D sine-wave estimated on large square windows (*)
: 2D sine-wave
Need of adaptive neighborhood
Need of new estimation technique
nHRLR iiii eeee
(*) E. Trouvé et al. “Improving phase unwrapping techniques by the use of local frequency”, IEEE Transactions on Geoscience and Remote Sensing, 36(6):1963-1972, 1998
LR
HR
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 8
Intensity Driven Adaptive Neighborhood (IDAN)
(*) G. Vasile et al. “Intensity-Driven-Adaptive-Neighborhood Technique for Polarimetric and Interferometric SAR Parameters Estimation”. IEEE Transactions on Geoscience and Remote Sensing, 44(5):1609-1621, 2006
2-step region growing technique (*)
Driven simultaneously on all the intensities of the input data set; AN makes it possible to reach the number of pixels necessary
for reliable estimation;
AN preserves the stationarity since most of the sources of phase nonstationaritymost of the sources of phase nonstationarityare revealed by the SAR intensity are revealed by the SAR intensity which is mostly influenced by the local slopewhich is mostly influenced by the local slope.
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 9
Estimation of the local frequency
2D phase model:
Estimation technique based on the autocorrelation function:
under stationarity and phase noise iid hypothesis K real
Step 1: estimation of on the Np,q available pixel pairs
Step 2: estimation of the local frequency:
)(2*),(),(),( yx qfpfjKeqlpkslksEqp
),( qp
),()1,(
),(1,,arg
2
1ˆ qpqpNNqp
qpqpf y
),(),1(
),(,1,arg
2
1ˆ qpqpNNqp
qpqpf x
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 10
Algorithm implementation
HR - IDANHR - IDANFrequency Frequency EstimationEstimation
2D - LR2D - LRlocal local
frequenciesfrequencies
SAR SAR intensities intensities
LR MUSICLR MUSICFrequencyFrequencyEstimationEstimation
SAR phaseSAR phase LR Freq.LR Freq.
CompensationCompensation
2D - HR2D - HRlocal local
frequenciesfrequencies
Local compensation of LR deterministic geometrical phase component
The resulting phase signal exhibits the local differences between the 2D sine-wave model and the real HR fringe pattern
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 11
Outlines
Context: InSAR high resolution Local frequency estimation algorithm Results and discussions
Low Resolution ERS TANDEM data High Resolution simulated TS-X data
Conclusions and perspectives
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 12
TANDEM ERS data set
masteramplitude phase
LR fringe orientation
HR fringe orientation
LUT
Mer-de-glace glacier [C-band, 5-looks, 768x489 pixels, 20x20 m, ea=45m]
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 13
TANDEM ERS data set
masteramplitude phase
LR+HR fringe
orientation
IDANfiltered phase
LUT
Mer-de-glace glacier [C-band, 5-looks, 768x489 pixels, 20x20 m, ea=45m]
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 14
TANDEM ERS data set
LR+HRfringe
orientation
phase
IDAN filtered coherence
ROI-PACfiltered
coherence
LUT
Mer-de-glace glacier [C-band, 5-looks, 768x489 pixels, 20x20 m, ea=45m]
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 15
TerraSAR-X application
(a) (b)
The Mer-de-glace glacier: (a) Aerotriangulation, (b) DTM 2mx2m.
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 16
TerraSAR-X application
Slant range sampling of the SAR intensity
Slant range sampling of the elevation (linear interpolation)
Descending pass simulation
1.2x2m, αin=30, H=514km
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 17
TerraSAR-X application
Simulated HR SAR amplitude: σ2=1
(speckle variance), 1.2x2m
Real LR ERS SAR amplitude: 20x20m
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 18
TerraSAR-X application
Simulated HR SAR amplitude: σ2=1
(speckle variance), 1.2x2m
Simulated HR SAR phase: ea=10m, uniform phase
noise distribution ±π/4
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 19
TerraSAR-X application
Simulated HR SAR phase: ea=10m, uniform phase noise distribution ±π/4
LUT
LR map:fringe orientation
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 20
TerraSAR-X application
LR map:fringe orientation
LUT
LR+HR map:fringe orientation
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 21
TerraSAR-X application
LR+HR map:fringe orientation
IDAN LR+HR filtered phase
LUT
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 22
TerraSAR-X applicationLUT
May 2004 Photo of the simulated TerraSAR-X region on the Mer-de-glace glacier (approximate position of the profile)
50m spatial profile along the surface of the Mer-de-glace glacier:
real altitude resampled in the TerraSAR-X slant range,unwrapped HR+LR estimates
of the local frequencies,unwrapped LR estimates of the local frequencies.
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 23
Conclusions and perspectives
ConclusionsConclusions:: HRHR frequency estimation combined with intensity driven frequency estimation combined with intensity driven
adaptive neighborhood;adaptive neighborhood; estimate local frequenciesestimate local frequencies within HR interferograms; within HR interferograms; measure the measure the local topographic variationslocal topographic variations in in
interferograms with a interferograms with a small altitude of ambiguitysmall altitude of ambiguity..
Future directions:Future directions: Chamonix – Mont Blanc Chamonix – Mont Blanc glacier monitoring glacier monitoring by D-InSAR,by D-InSAR, New context: POL-InSAR airborne data.New context: POL-InSAR airborne data.
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 24
E-SAR CampaignE-SAR Campaign
Argentière: Oct./06 & Feb./07Argentière: Oct./06 & Feb./07
7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 25
Thank you!
This work was supported by the French national project ACI-MEGATOR. The authors wish to thank the European Space Agency for providing the SAR data through the Category 1 proposal No.3525.
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